Overview

Dataset statistics

Number of variables40
Number of observations5922
Missing cells3386
Missing cells (%)1.4%
Duplicate rows16
Duplicate rows (%)0.3%
Total size in memory1.6 MiB
Average record size in memory289.0 B

Variable types

Categorical21
Numeric15
Text2
DateTime1
Boolean1

Alerts

Dataset has 16 (0.3%) duplicate rowsDuplicates
Arabic is highly imbalanced (74.3%)Imbalance
Bengali is highly imbalanced (73.3%)Imbalance
Chinese is highly imbalanced (86.9%)Imbalance
Dutch is highly imbalanced (88.5%)Imbalance
English is highly imbalanced (92.4%)Imbalance
Hebrew is highly imbalanced (91.1%)Imbalance
Hindi is highly imbalanced (55.0%)Imbalance
Indonesian is highly imbalanced (89.4%)Imbalance
Italian is highly imbalanced (73.0%)Imbalance
Portuguese is highly imbalanced (82.2%)Imbalance
Punjabi is highly imbalanced (85.4%)Imbalance
Russian is highly imbalanced (77.7%)Imbalance
Turkish is highly imbalanced (90.3%)Imbalance
Ukrainian is highly imbalanced (86.1%)Imbalance
Rating has 104 (1.8%) missing valuesMissing
Member Since has 1571 (26.5%) missing valuesMissing
Avg Response Time has 1685 (28.5%) missing valuesMissing
Basic Price is highly skewed (γ1 = 20.51586071)Skewed
Basic Revision has 1114 (18.8%) zerosZeros
Standard Revision has 1031 (17.4%) zerosZeros
Premium Revision has 1281 (21.6%) zerosZeros
Last Delivery has 1273 (21.5%) zerosZeros
Order in Queue has 3761 (63.5%) zerosZeros

Reproduction

Analysis started2024-05-26 14:17:14.957785
Analysis finished2024-05-26 14:17:45.156301
Duration30.2 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

Category
Categorical

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size92.5 KiB
Music & Audio
734 
Programming & Tech
722 
Business
719 
Data
641 
Lifestyle
631 
Other values (5)
2475 

Length

Max length21
Median length17
Mean length13.288416
Min length4

Characters and Unicode

Total characters78694
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowData
2nd rowData
3rd rowData
4th rowData
5th rowData

Common Values

ValueCountFrequency (%)
Music & Audio 734
12.4%
Programming & Tech 722
12.2%
Business 719
12.1%
Data 641
10.8%
Lifestyle 631
10.7%
Writing & Translation 551
9.3%
Graphics & Design 545
9.2%
Video & Animation 514
8.7%
Digital Marketing 512
8.6%
Photography 353
6.0%

Length

2024-05-26T17:47:45.301303image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-26T17:47:45.469335image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
3066
24.4%
music 734
 
5.8%
audio 734
 
5.8%
programming 722
 
5.7%
tech 722
 
5.7%
business 719
 
5.7%
data 641
 
5.1%
lifestyle 631
 
5.0%
translation 551
 
4.4%
writing 551
 
4.4%
Other values (7) 3495
27.8%

Most occurring characters

ValueCountFrequency (%)
i 9361
 
11.9%
6644
 
8.4%
a 5542
 
7.0%
n 5179
 
6.6%
s 5163
 
6.6%
e 4274
 
5.4%
t 4265
 
5.4%
r 3956
 
5.0%
g 3917
 
5.0%
o 3741
 
4.8%
Other values (21) 26652
33.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 78694
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 9361
 
11.9%
6644
 
8.4%
a 5542
 
7.0%
n 5179
 
6.6%
s 5163
 
6.6%
e 4274
 
5.4%
t 4265
 
5.4%
r 3956
 
5.0%
g 3917
 
5.0%
o 3741
 
4.8%
Other values (21) 26652
33.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 78694
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 9361
 
11.9%
6644
 
8.4%
a 5542
 
7.0%
n 5179
 
6.6%
s 5163
 
6.6%
e 4274
 
5.4%
t 4265
 
5.4%
r 3956
 
5.0%
g 3917
 
5.0%
o 3741
 
4.8%
Other values (21) 26652
33.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 78694
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 9361
 
11.9%
6644
 
8.4%
a 5542
 
7.0%
n 5179
 
6.6%
s 5163
 
6.6%
e 4274
 
5.4%
t 4265
 
5.4%
r 3956
 
5.0%
g 3917
 
5.0%
o 3741
 
4.8%
Other values (21) 26652
33.9%

Field
Categorical

Distinct35
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size92.5 KiB
video-editing
 
190
articles-blogposts
 
189
mixing-mastering
 
188
social-media-design
 
186
game-development
 
185
Other values (30)
4984 

Length

Max length29
Median length20
Mean length15.881966
Min length5

Characters and Unicode

Total characters94053
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdata-engineering
2nd rowdata-engineering
3rd rowdata-engineering
4th rowdata-engineering
5th rowdata-engineering

Common Values

ValueCountFrequency (%)
video-editing 190
 
3.2%
articles-blogposts 189
 
3.2%
mixing-mastering 188
 
3.2%
social-media-design 186
 
3.1%
game-development 185
 
3.1%
producers 185
 
3.1%
financial-consulting-services 185
 
3.1%
software-development 185
 
3.1%
game-art 184
 
3.1%
business-plans 184
 
3.1%
Other values (25) 4061
68.6%

Length

2024-05-26T17:47:45.694299image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
video-editing 190
 
3.2%
articles-blogposts 189
 
3.2%
mixing-mastering 188
 
3.2%
social-media-design 186
 
3.1%
game-development 185
 
3.1%
producers 185
 
3.1%
financial-consulting-services 185
 
3.1%
software-development 185
 
3.1%
business-plans 184
 
3.1%
game-art 184
 
3.1%
Other values (25) 4061
68.6%

Most occurring characters

ValueCountFrequency (%)
e 11199
11.9%
s 8804
 
9.4%
i 8530
 
9.1%
a 7211
 
7.7%
n 7138
 
7.6%
t 6486
 
6.9%
- 6181
 
6.6%
o 5615
 
6.0%
r 4616
 
4.9%
g 4050
 
4.3%
Other values (15) 24223
25.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 94053
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 11199
11.9%
s 8804
 
9.4%
i 8530
 
9.1%
a 7211
 
7.7%
n 7138
 
7.6%
t 6486
 
6.9%
- 6181
 
6.6%
o 5615
 
6.0%
r 4616
 
4.9%
g 4050
 
4.3%
Other values (15) 24223
25.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 94053
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 11199
11.9%
s 8804
 
9.4%
i 8530
 
9.1%
a 7211
 
7.7%
n 7138
 
7.6%
t 6486
 
6.9%
- 6181
 
6.6%
o 5615
 
6.0%
r 4616
 
4.9%
g 4050
 
4.3%
Other values (15) 24223
25.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 94053
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 11199
11.9%
s 8804
 
9.4%
i 8530
 
9.1%
a 7211
 
7.7%
n 7138
 
7.6%
t 6486
 
6.9%
- 6181
 
6.6%
o 5615
 
6.0%
r 4616
 
4.9%
g 4050
 
4.3%
Other values (15) 24223
25.8%

Seller Level
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size92.5 KiB
new seller
1662 
level 1
1538 
level 2
1468 
top rated seller
1254 

Length

Max length16
Median length7
Mean length9.7477204
Min length7

Characters and Unicode

Total characters57726
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowlevel 1
2nd rowlevel 1
3rd rowlevel 1
4th rowlevel 1
5th rowlevel 1

Common Values

ValueCountFrequency (%)
new seller 1662
28.1%
level 1 1538
26.0%
level 2 1468
24.8%
top rated seller 1254
21.2%

Length

2024-05-26T17:47:45.841333image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-26T17:47:45.967301image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
level 3006
23.0%
seller 2916
22.3%
new 1662
12.7%
1 1538
11.7%
2 1468
11.2%
top 1254
9.6%
rated 1254
9.6%

Most occurring characters

ValueCountFrequency (%)
e 14760
25.6%
l 11844
20.5%
7176
12.4%
r 4170
 
7.2%
v 3006
 
5.2%
s 2916
 
5.1%
t 2508
 
4.3%
n 1662
 
2.9%
w 1662
 
2.9%
1 1538
 
2.7%
Other values (5) 6484
11.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 57726
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 14760
25.6%
l 11844
20.5%
7176
12.4%
r 4170
 
7.2%
v 3006
 
5.2%
s 2916
 
5.1%
t 2508
 
4.3%
n 1662
 
2.9%
w 1662
 
2.9%
1 1538
 
2.7%
Other values (5) 6484
11.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 57726
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 14760
25.6%
l 11844
20.5%
7176
12.4%
r 4170
 
7.2%
v 3006
 
5.2%
s 2916
 
5.1%
t 2508
 
4.3%
n 1662
 
2.9%
w 1662
 
2.9%
1 1538
 
2.7%
Other values (5) 6484
11.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 57726
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 14760
25.6%
l 11844
20.5%
7176
12.4%
r 4170
 
7.2%
v 3006
 
5.2%
s 2916
 
5.1%
t 2508
 
4.3%
n 1662
 
2.9%
w 1662
 
2.9%
1 1538
 
2.7%
Other values (5) 6484
11.2%

Seller In Same Level
Real number (ℝ)

Distinct117
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6237.7379
Minimum1
Maximum120000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size69.4 KiB
2024-05-26T17:47:46.132342image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile38
Q1329
median1100
Q35500
95-th percentile33000
Maximum120000
Range119999
Interquartile range (IQR)5171

Descriptive statistics

Standard deviation15193.97
Coefficient of variation (CV)2.4358142
Kurtosis27.200425
Mean6237.7379
Median Absolute Deviation (MAD)974
Skewness4.7601049
Sum36939884
Variance2.3085674 × 108
MonotonicityNot monotonic
2024-05-26T17:47:46.363376image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 226
 
3.8%
2800 183
 
3.1%
2000 133
 
2.2%
313 130
 
2.2%
8900 96
 
1.6%
2200 95
 
1.6%
1800 94
 
1.6%
2700 94
 
1.6%
12000 93
 
1.6%
606 92
 
1.6%
Other values (107) 4686
79.1%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 2
 
< 0.1%
7 7
 
0.1%
9 9
 
0.2%
18 20
 
0.3%
22 24
 
0.4%
23 68
1.1%
25 26
 
0.4%
27 27
 
0.5%
32 32
0.5%
ValueCountFrequency (%)
120000 47
0.8%
75000 46
0.8%
53000 48
0.8%
50472 48
0.8%
44000 48
0.8%
41000 46
0.8%
33000 48
0.8%
26000 48
0.8%
23000 46
0.8%
19000 45
0.8%

Basic Price
Real number (ℝ)

SKEWED 

Distinct259
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean132.54156
Minimum3.24
Maximum20000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.5 KiB
2024-05-26T17:47:46.562341image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum3.24
5-th percentile5
Q115
median40
Q3100
95-th percentile400
Maximum20000
Range19996.76
Interquartile range (IQR)85

Descriptive statistics

Standard deviation572.93752
Coefficient of variation (CV)4.3227009
Kurtosis583.34659
Mean132.54156
Median Absolute Deviation (MAD)30
Skewness20.515861
Sum784911.13
Variance328257.41
MonotonicityNot monotonic
2024-05-26T17:47:46.817342image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 692
 
11.7%
5 495
 
8.4%
100 422
 
7.1%
30 376
 
6.3%
15 338
 
5.7%
50 326
 
5.5%
20 311
 
5.3%
25 243
 
4.1%
150 192
 
3.2%
40 167
 
2.8%
Other values (249) 2360
39.9%
ValueCountFrequency (%)
3.24 6
 
0.1%
4.48 44
 
0.7%
5 495
8.4%
6.49 2
 
< 0.1%
8.95 59
 
1.0%
8.96 1
 
< 0.1%
9.73 1
 
< 0.1%
10 692
11.7%
13.44 33
 
0.6%
15 338
5.7%
ValueCountFrequency (%)
20000 2
< 0.1%
11400 1
 
< 0.1%
10529.63 1
 
< 0.1%
10000 2
< 0.1%
9248.15 1
 
< 0.1%
7500 1
 
< 0.1%
6500 1
 
< 0.1%
4500 1
 
< 0.1%
4000 4
0.1%
3800 1
 
< 0.1%

Standard Price
Real number (ℝ)

Distinct393
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean275.41253
Minimum3.24
Maximum20000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.5 KiB
2024-05-26T17:47:47.004376image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum3.24
5-th percentile10
Q135.82
median89.56
Q3200
95-th percentile990
Maximum20000
Range19996.76
Interquartile range (IQR)164.18

Descriptive statistics

Standard deviation867.33119
Coefficient of variation (CV)3.1492075
Kurtosis185.7167
Mean275.41253
Median Absolute Deviation (MAD)64.56
Skewness11.306609
Sum1630993
Variance752263.4
MonotonicityNot monotonic
2024-05-26T17:47:47.188375image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 356
 
6.0%
20 263
 
4.4%
100 243
 
4.1%
30 235
 
4.0%
10 233
 
3.9%
150 227
 
3.8%
25 226
 
3.8%
40 202
 
3.4%
15 195
 
3.3%
200 182
 
3.1%
Other values (383) 3560
60.1%
ValueCountFrequency (%)
3.24 1
 
< 0.1%
4.48 8
 
0.1%
5 79
 
1.3%
6.49 2
 
< 0.1%
8.95 23
 
0.4%
8.96 1
 
< 0.1%
9.73 3
 
0.1%
10 233
3.9%
12.98 1
 
< 0.1%
13.44 20
 
0.3%
ValueCountFrequency (%)
20000 2
< 0.1%
18496.3 1
< 0.1%
15581.48 1
< 0.1%
15000 1
< 0.1%
12000 1
< 0.1%
11400 1
< 0.1%
10670.37 1
< 0.1%
10000 2
< 0.1%
9750 1
< 0.1%
8000 2
< 0.1%

Premium Price
Real number (ℝ)

Distinct489
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean506.82098
Minimum3.24
Maximum35566.67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.5 KiB
2024-05-26T17:47:47.370377image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum3.24
5-th percentile17.91
Q160
median150
Q3360
95-th percentile1904.75
Maximum35566.67
Range35563.43
Interquartile range (IQR)300

Descriptive statistics

Standard deviation1560.4839
Coefficient of variation (CV)3.0789647
Kurtosis145.75613
Mean506.82098
Median Absolute Deviation (MAD)105.22
Skewness9.7935548
Sum3001393.9
Variance2435110.1
MonotonicityNot monotonic
2024-05-26T17:47:47.552342image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 365
 
6.2%
50 237
 
4.0%
200 220
 
3.7%
150 217
 
3.7%
30 167
 
2.8%
300 156
 
2.6%
250 150
 
2.5%
500 143
 
2.4%
60 140
 
2.4%
20 138
 
2.3%
Other values (479) 3989
67.4%
ValueCountFrequency (%)
3.24 1
 
< 0.1%
4.48 8
 
0.1%
5 79
1.3%
6.49 1
 
< 0.1%
8.95 9
 
0.2%
10 71
1.2%
12.98 3
 
0.1%
13.44 10
 
0.2%
15 113
1.9%
16.22 1
 
< 0.1%
ValueCountFrequency (%)
35566.67 2
 
< 0.1%
29307.41 1
 
< 0.1%
20000 6
0.1%
15000 3
0.1%
14229.63 2
 
< 0.1%
13659.26 1
 
< 0.1%
13500 1
 
< 0.1%
12000 1
 
< 0.1%
11400 1
 
< 0.1%
10800 1
 
< 0.1%

Basic Delivery
Real number (ℝ)

Distinct15
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2951706
Minimum1
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size69.4 KiB
2024-05-26T17:47:47.702375image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile14
Maximum90
Range89
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.494313
Coefficient of variation (CV)1.2791839
Kurtosis53.835776
Mean4.2951706
Median Absolute Deviation (MAD)1
Skewness5.5233996
Sum25436
Variance30.187475
MonotonicityNot monotonic
2024-05-26T17:47:47.832372image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2 1447
24.4%
1 1235
20.9%
3 1223
20.7%
7 521
 
8.8%
5 498
 
8.4%
4 361
 
6.1%
10 211
 
3.6%
14 179
 
3.0%
30 92
 
1.6%
6 73
 
1.2%
Other values (5) 82
 
1.4%
ValueCountFrequency (%)
1 1235
20.9%
2 1447
24.4%
3 1223
20.7%
4 361
 
6.1%
5 498
 
8.4%
6 73
 
1.2%
7 521
 
8.8%
10 211
 
3.6%
14 179
 
3.0%
21 69
 
1.2%
ValueCountFrequency (%)
90 4
 
0.1%
60 3
 
0.1%
45 5
 
0.1%
30 92
 
1.6%
29 1
 
< 0.1%
21 69
 
1.2%
14 179
 
3.0%
10 211
3.6%
7 521
8.8%
6 73
 
1.2%

Standard Delivery
Real number (ℝ)

Distinct17
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3069909
Minimum1
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size69.4 KiB
2024-05-26T17:47:47.963429image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q37
95-th percentile21
Maximum90
Range89
Interquartile range (IQR)4

Descriptive statistics

Standard deviation7.3559566
Coefficient of variation (CV)1.1663179
Kurtosis25.656314
Mean6.3069909
Median Absolute Deviation (MAD)2
Skewness4.0918156
Sum37350
Variance54.110098
MonotonicityNot monotonic
2024-05-26T17:47:48.092021image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
3 1106
18.7%
2 1006
17.0%
5 777
13.1%
7 709
12.0%
4 613
10.4%
1 453
7.6%
10 381
 
6.4%
14 324
 
5.5%
6 184
 
3.1%
30 161
 
2.7%
Other values (7) 208
 
3.5%
ValueCountFrequency (%)
1 453
7.6%
2 1006
17.0%
3 1106
18.7%
4 613
10.4%
5 777
13.1%
6 184
 
3.1%
7 709
12.0%
10 381
 
6.4%
14 324
 
5.5%
15 2
 
< 0.1%
ValueCountFrequency (%)
90 4
 
0.1%
75 2
 
< 0.1%
60 17
 
0.3%
45 24
 
0.4%
30 161
2.7%
29 1
 
< 0.1%
21 158
2.7%
15 2
 
< 0.1%
14 324
5.5%
10 381
6.4%

Premium Delivery
Real number (ℝ)

Distinct19
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.2051672
Minimum1
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size69.4 KiB
2024-05-26T17:47:48.220052image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q310
95-th percentile30
Maximum90
Range89
Interquartile range (IQR)7

Descriptive statistics

Standard deviation11.406506
Coefficient of variation (CV)1.2391417
Kurtosis18.223261
Mean9.2051672
Median Absolute Deviation (MAD)3
Skewness3.6885772
Sum54513
Variance130.10838
MonotonicityNot monotonic
2024-05-26T17:47:48.352061image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
7 966
16.3%
3 829
14.0%
5 742
12.5%
10 589
9.9%
2 532
9.0%
4 498
8.4%
14 457
7.7%
1 353
 
6.0%
30 352
 
5.9%
6 231
 
3.9%
Other values (9) 373
 
6.3%
ValueCountFrequency (%)
1 353
 
6.0%
2 532
9.0%
3 829
14.0%
4 498
8.4%
5 742
12.5%
6 231
 
3.9%
7 966
16.3%
8 2
 
< 0.1%
10 589
9.9%
14 457
7.7%
ValueCountFrequency (%)
90 35
 
0.6%
75 9
 
0.2%
60 43
 
0.7%
45 63
 
1.1%
30 352
5.9%
29 1
 
< 0.1%
28 1
 
< 0.1%
21 218
3.7%
20 1
 
< 0.1%
14 457
7.7%

Basic Revision
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5829112
Minimum-1
Maximum100
Zeros1114
Zeros (%)18.8%
Negative2340
Negative (%)39.5%
Memory size69.4 KiB
2024-05-26T17:47:48.483020image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median0
Q31
95-th percentile3
Maximum100
Range101
Interquartile range (IQR)2

Descriptive statistics

Standard deviation10.952275
Coefficient of variation (CV)6.9190714
Kurtosis75.265824
Mean1.5829112
Median Absolute Deviation (MAD)1
Skewness8.699978
Sum9374
Variance119.95234
MonotonicityNot monotonic
2024-05-26T17:47:48.618854image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
-1 2340
39.5%
1 1147
19.4%
0 1114
18.8%
2 710
 
12.0%
3 352
 
5.9%
5 111
 
1.9%
100 71
 
1.2%
4 44
 
0.7%
9 17
 
0.3%
6 8
 
0.1%
Other values (2) 8
 
0.1%
ValueCountFrequency (%)
-1 2340
39.5%
0 1114
18.8%
1 1147
19.4%
2 710
 
12.0%
3 352
 
5.9%
4 44
 
0.7%
5 111
 
1.9%
6 8
 
0.1%
7 5
 
0.1%
8 3
 
0.1%
ValueCountFrequency (%)
100 71
 
1.2%
9 17
 
0.3%
8 3
 
0.1%
7 5
 
0.1%
6 8
 
0.1%
5 111
 
1.9%
4 44
 
0.7%
3 352
 
5.9%
2 710
12.0%
1 1147
19.4%

Standard Revision
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8593381
Minimum-1
Maximum100
Zeros1031
Zeros (%)17.4%
Negative2340
Negative (%)39.5%
Memory size69.4 KiB
2024-05-26T17:47:48.750822image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median0
Q32
95-th percentile5
Maximum100
Range101
Interquartile range (IQR)3

Descriptive statistics

Standard deviation10.980828
Coefficient of variation (CV)5.9057729
Kurtosis73.59006
Mean1.8593381
Median Absolute Deviation (MAD)1
Skewness8.5595563
Sum11011
Variance120.57859
MonotonicityNot monotonic
2024-05-26T17:47:48.891821image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
-1 2340
39.5%
0 1031
17.4%
2 887
 
15.0%
1 668
 
11.3%
3 484
 
8.2%
5 218
 
3.7%
4 117
 
2.0%
100 71
 
1.2%
9 37
 
0.6%
6 31
 
0.5%
Other values (2) 38
 
0.6%
ValueCountFrequency (%)
-1 2340
39.5%
0 1031
17.4%
1 668
 
11.3%
2 887
 
15.0%
3 484
 
8.2%
4 117
 
2.0%
5 218
 
3.7%
6 31
 
0.5%
7 24
 
0.4%
8 14
 
0.2%
ValueCountFrequency (%)
100 71
 
1.2%
9 37
 
0.6%
8 14
 
0.2%
7 24
 
0.4%
6 31
 
0.5%
5 218
 
3.7%
4 117
 
2.0%
3 484
8.2%
2 887
15.0%
1 668
11.3%

Premium Revision
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9167511
Minimum-1
Maximum100
Zeros1281
Zeros (%)21.6%
Negative2340
Negative (%)39.5%
Memory size69.4 KiB
2024-05-26T17:47:49.023853image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median0
Q32
95-th percentile5
Maximum100
Range101
Interquartile range (IQR)3

Descriptive statistics

Standard deviation11.009446
Coefficient of variation (CV)5.7438057
Kurtosis72.620937
Mean1.9167511
Median Absolute Deviation (MAD)1
Skewness8.4793511
Sum11351
Variance121.2079
MonotonicityNot monotonic
2024-05-26T17:47:49.156428image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
-1 2340
39.5%
0 1281
21.6%
2 631
 
10.7%
3 583
 
9.8%
1 456
 
7.7%
5 254
 
4.3%
4 136
 
2.3%
100 71
 
1.2%
9 66
 
1.1%
7 42
 
0.7%
Other values (2) 62
 
1.0%
ValueCountFrequency (%)
-1 2340
39.5%
0 1281
21.6%
1 456
 
7.7%
2 631
 
10.7%
3 583
 
9.8%
4 136
 
2.3%
5 254
 
4.3%
6 37
 
0.6%
7 42
 
0.7%
8 25
 
0.4%
ValueCountFrequency (%)
100 71
 
1.2%
9 66
 
1.1%
8 25
 
0.4%
7 42
 
0.7%
6 37
 
0.6%
5 254
4.3%
4 136
 
2.3%
3 583
9.8%
2 631
10.7%
1 456
7.7%

Rating
Real number (ℝ)

MISSING 

Distinct20
Distinct (%)0.3%
Missing104
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean4.9345308
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.5 KiB
2024-05-26T17:47:49.293109image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.8
Q14.9
median5
Q35
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.19144177
Coefficient of variation (CV)0.038796348
Kurtosis242.14932
Mean4.9345308
Median Absolute Deviation (MAD)0
Skewness-13.293237
Sum28709.1
Variance0.036649952
MonotonicityNot monotonic
2024-05-26T17:47:49.442108image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
5 3595
60.7%
4.9 1599
27.0%
4.8 390
 
6.6%
4.7 121
 
2.0%
4.6 32
 
0.5%
4.5 21
 
0.4%
4.3 16
 
0.3%
4 11
 
0.2%
1 7
 
0.1%
4.4 6
 
0.1%
Other values (10) 20
 
0.3%
(Missing) 104
 
1.8%
ValueCountFrequency (%)
1 7
0.1%
1.7 1
 
< 0.1%
2.8 1
 
< 0.1%
3 4
 
0.1%
3.2 2
 
< 0.1%
3.3 1
 
< 0.1%
3.5 1
 
< 0.1%
3.7 1
 
< 0.1%
3.8 1
 
< 0.1%
4 11
0.2%
ValueCountFrequency (%)
5 3595
60.7%
4.9 1599
27.0%
4.8 390
 
6.6%
4.7 121
 
2.0%
4.6 32
 
0.5%
4.5 21
 
0.4%
4.4 6
 
0.1%
4.3 16
 
0.3%
4.2 4
 
0.1%
4.1 4
 
0.1%

Rating Count
Real number (ℝ)

Distinct908
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean207.38872
Minimum-1
Maximum12860
Zeros0
Zeros (%)0.0%
Negative104
Negative (%)1.8%
Memory size92.5 KiB
2024-05-26T17:47:49.619124image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q112
median40
Q3155.75
95-th percentile881.95
Maximum12860
Range12861
Interquartile range (IQR)143.75

Descriptive statistics

Standard deviation617.03038
Coefficient of variation (CV)2.975236
Kurtosis138.34583
Mean207.38872
Median Absolute Deviation (MAD)36
Skewness9.6118637
Sum1228156
Variance380726.49
MonotonicityNot monotonic
2024-05-26T17:47:49.800125image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 194
 
3.3%
3 174
 
2.9%
2 169
 
2.9%
5 121
 
2.0%
4 118
 
2.0%
-1 104
 
1.8%
7 100
 
1.7%
11 99
 
1.7%
12 96
 
1.6%
15 93
 
1.6%
Other values (898) 4654
78.6%
ValueCountFrequency (%)
-1 104
1.8%
1 194
3.3%
2 169
2.9%
3 174
2.9%
4 118
2.0%
5 121
2.0%
6 89
1.5%
7 100
1.7%
8 73
 
1.2%
9 73
 
1.2%
ValueCountFrequency (%)
12860 1
< 0.1%
12396 1
< 0.1%
11201 1
< 0.1%
10938 1
< 0.1%
10732 1
< 0.1%
9729 1
< 0.1%
8847 1
< 0.1%
8792 1
< 0.1%
5971 1
< 0.1%
5961 1
< 0.1%
Distinct113
Distinct (%)1.9%
Missing1
Missing (%)< 0.1%
Memory size92.5 KiB
2024-05-26T17:47:50.035124image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length22
Median length19
Mean length8.7098463
Min length4

Characters and Unicode

Total characters51571
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)0.3%

Sample

1st rowPakistan
2nd rowPakistan
3rd rowPakistan
4th rowUnited States
5th rowUnited Kingdom
ValueCountFrequency (%)
pakistan 1441
19.8%
united 1123
15.4%
states 744
 
10.2%
bangladesh 487
 
6.7%
india 436
 
6.0%
kingdom 352
 
4.8%
nigeria 144
 
2.0%
germany 123
 
1.7%
sri 123
 
1.7%
lanka 123
 
1.7%
Other values (118) 2200
30.2%
2024-05-26T17:47:50.613242image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 7856
15.2%
n 5424
 
10.5%
i 5044
 
9.8%
t 4551
 
8.8%
e 3933
 
7.6%
s 3051
 
5.9%
d 2847
 
5.5%
k 1740
 
3.4%
P 1575
 
3.1%
1375
 
2.7%
Other values (42) 14175
27.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 51571
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 7856
15.2%
n 5424
 
10.5%
i 5044
 
9.8%
t 4551
 
8.8%
e 3933
 
7.6%
s 3051
 
5.9%
d 2847
 
5.5%
k 1740
 
3.4%
P 1575
 
3.1%
1375
 
2.7%
Other values (42) 14175
27.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 51571
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 7856
15.2%
n 5424
 
10.5%
i 5044
 
9.8%
t 4551
 
8.8%
e 3933
 
7.6%
s 3051
 
5.9%
d 2847
 
5.5%
k 1740
 
3.4%
P 1575
 
3.1%
1375
 
2.7%
Other values (42) 14175
27.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 51571
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 7856
15.2%
n 5424
 
10.5%
i 5044
 
9.8%
t 4551
 
8.8%
e 3933
 
7.6%
s 3051
 
5.9%
d 2847
 
5.5%
k 1740
 
3.4%
P 1575
 
3.1%
1375
 
2.7%
Other values (42) 14175
27.5%

Member Since
Date

MISSING 

Distinct149
Distinct (%)3.4%
Missing1571
Missing (%)26.5%
Memory size92.5 KiB
Minimum2011-03-01 00:00:00
Maximum2024-05-01 00:00:00
2024-05-26T17:47:50.794453image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:50.972458image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Avg Response Time
Real number (ℝ)

MISSING 

Distinct34
Distinct (%)0.8%
Missing1685
Missing (%)28.5%
Infinite0
Infinite (%)0.0%
Mean4.4798206
Minimum1
Maximum696
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.5 KiB
2024-05-26T17:47:51.126538image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile13
Maximum696
Range695
Interquartile range (IQR)2

Descriptive statistics

Standard deviation18.708852
Coefficient of variation (CV)4.1762502
Kurtosis531.12124
Mean4.4798206
Median Absolute Deviation (MAD)0
Skewness18.884795
Sum18981
Variance350.02113
MonotonicityNot monotonic
2024-05-26T17:47:51.279853image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
1 2544
43.0%
2 505
 
8.5%
3 300
 
5.1%
4 221
 
3.7%
5 131
 
2.2%
6 90
 
1.5%
7 58
 
1.0%
8 55
 
0.9%
9 46
 
0.8%
24 45
 
0.8%
Other values (24) 242
 
4.1%
(Missing) 1685
28.5%
ValueCountFrequency (%)
1 2544
43.0%
2 505
 
8.5%
3 300
 
5.1%
4 221
 
3.7%
5 131
 
2.2%
6 90
 
1.5%
7 58
 
1.0%
8 55
 
0.9%
9 46
 
0.8%
10 31
 
0.5%
ValueCountFrequency (%)
696 1
 
< 0.1%
312 1
 
< 0.1%
288 4
 
0.1%
264 2
 
< 0.1%
168 1
 
< 0.1%
144 6
 
0.1%
120 1
 
< 0.1%
96 8
 
0.1%
72 19
0.3%
48 37
0.6%

Last Delivery
Real number (ℝ)

ZEROS 

Distinct27
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.437859
Minimum-1
Maximum1095
Zeros1273
Zeros (%)21.5%
Negative1574
Negative (%)26.6%
Memory size92.5 KiB
2024-05-26T17:47:51.432821image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median1
Q36
95-th percentile90
Maximum1095
Range1096
Interquartile range (IQR)7

Descriptive statistics

Standard deviation56.48062
Coefficient of variation (CV)3.6585786
Kurtosis62.979231
Mean15.437859
Median Absolute Deviation (MAD)2
Skewness6.5937483
Sum91423
Variance3190.0604
MonotonicityNot monotonic
2024-05-26T17:47:51.576776image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
-1 1574
26.6%
0 1273
21.5%
1 605
 
10.2%
7 450
 
7.6%
2 373
 
6.3%
3 261
 
4.4%
30 210
 
3.5%
14 209
 
3.5%
4 185
 
3.1%
5 145
 
2.4%
Other values (17) 637
10.8%
ValueCountFrequency (%)
-1 1574
26.6%
0 1273
21.5%
1 605
 
10.2%
2 373
 
6.3%
3 261
 
4.4%
4 185
 
3.1%
5 145
 
2.4%
6 67
 
1.1%
7 450
 
7.6%
14 209
 
3.5%
ValueCountFrequency (%)
1095 1
 
< 0.1%
730 6
 
0.1%
365 36
0.6%
360 1
 
< 0.1%
330 15
 
0.3%
300 22
0.4%
270 10
 
0.2%
240 23
0.4%
210 50
0.8%
180 23
0.4%
Distinct842
Distinct (%)14.3%
Missing25
Missing (%)0.4%
Memory size92.5 KiB
2024-05-26T17:47:51.764154image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length42
Median length38
Mean length15.629642
Min length4

Characters and Unicode

Total characters92168
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique480 ?
Unique (%)8.1%

Sample

1st rowUrdu,English
2nd rowEnglish,Urdu
3rd rowUrdu,English
4th rowEnglish
5th rowEnglish,Polish
ValueCountFrequency (%)
english 1905
32.2%
english,spanish 324
 
5.5%
urdu,english 269
 
4.5%
english,french 133
 
2.2%
english,german 113
 
1.9%
bengali,english 102
 
1.7%
english,urdu 94
 
1.6%
hindi,english 80
 
1.4%
english,italian 71
 
1.2%
english,hindi 69
 
1.2%
Other values (841) 2758
46.6%
2024-05-26T17:47:52.143356image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 11342
12.3%
i 10477
11.4%
h 8473
9.2%
s 8181
8.9%
, 6951
 
7.5%
l 6764
 
7.3%
g 6448
 
7.0%
E 5876
 
6.4%
a 4870
 
5.3%
r 3463
 
3.8%
Other values (42) 19323
21.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 92168
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 11342
12.3%
i 10477
11.4%
h 8473
9.2%
s 8181
8.9%
, 6951
 
7.5%
l 6764
 
7.3%
g 6448
 
7.0%
E 5876
 
6.4%
a 4870
 
5.3%
r 3463
 
3.8%
Other values (42) 19323
21.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 92168
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 11342
12.3%
i 10477
11.4%
h 8473
9.2%
s 8181
8.9%
, 6951
 
7.5%
l 6764
 
7.3%
g 6448
 
7.0%
E 5876
 
6.4%
a 4870
 
5.3%
r 3463
 
3.8%
Other values (42) 19323
21.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 92168
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 11342
12.3%
i 10477
11.4%
h 8473
9.2%
s 8181
8.9%
, 6951
 
7.5%
l 6764
 
7.3%
g 6448
 
7.0%
E 5876
 
6.4%
a 4870
 
5.3%
r 3463
 
3.8%
Other values (42) 19323
21.0%

Order in Queue
Real number (ℝ)

ZEROS 

Distinct52
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5121581
Minimum0
Maximum215
Zeros3761
Zeros (%)63.5%
Negative0
Negative (%)0.0%
Memory size69.4 KiB
2024-05-26T17:47:52.320388image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile6
Maximum215
Range215
Interquartile range (IQR)1

Descriptive statistics

Standard deviation5.8154879
Coefficient of variation (CV)3.8458201
Kurtosis530.18101
Mean1.5121581
Median Absolute Deviation (MAD)0
Skewness18.543462
Sum8955
Variance33.819899
MonotonicityNot monotonic
2024-05-26T17:47:52.485242image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3761
63.5%
1 822
 
13.9%
2 443
 
7.5%
3 249
 
4.2%
4 159
 
2.7%
5 109
 
1.8%
6 85
 
1.4%
7 50
 
0.8%
8 44
 
0.7%
11 31
 
0.5%
Other values (42) 169
 
2.9%
ValueCountFrequency (%)
0 3761
63.5%
1 822
 
13.9%
2 443
 
7.5%
3 249
 
4.2%
4 159
 
2.7%
5 109
 
1.8%
6 85
 
1.4%
7 50
 
0.8%
8 44
 
0.7%
9 20
 
0.3%
ValueCountFrequency (%)
215 1
< 0.1%
164 1
< 0.1%
161 1
< 0.1%
82 1
< 0.1%
76 1
< 0.1%
72 1
< 0.1%
63 1
< 0.1%
56 2
< 0.1%
54 1
< 0.1%
52 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size52.0 KiB
False
5249 
True
673 
ValueCountFrequency (%)
False 5249
88.6%
True 673
 
11.4%
2024-05-26T17:47:52.615283image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Arabic
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size92.5 KiB
0
5666 
1
 
256

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5922
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5666
95.7%
1 256
 
4.3%

Length

2024-05-26T17:47:52.744502image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-26T17:47:52.858544image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 5666
95.7%
1 256
 
4.3%

Most occurring characters

ValueCountFrequency (%)
0 5666
95.7%
1 256
 
4.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5666
95.7%
1 256
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5666
95.7%
1 256
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5666
95.7%
1 256
 
4.3%

Bengali
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size92.5 KiB
0
5652 
1
 
270

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5922
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5652
95.4%
1 270
 
4.6%

Length

2024-05-26T17:47:52.981552image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-26T17:47:53.094630image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 5652
95.4%
1 270
 
4.6%

Most occurring characters

ValueCountFrequency (%)
0 5652
95.4%
1 270
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5652
95.4%
1 270
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5652
95.4%
1 270
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5652
95.4%
1 270
 
4.6%

Chinese
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size92.5 KiB
0
5814 
1
 
108

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5922
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5814
98.2%
1 108
 
1.8%

Length

2024-05-26T17:47:53.219715image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-26T17:47:53.332942image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 5814
98.2%
1 108
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 5814
98.2%
1 108
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5814
98.2%
1 108
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5814
98.2%
1 108
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5814
98.2%
1 108
 
1.8%

Dutch
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size92.5 KiB
0
5831 
1
 
91

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5922
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5831
98.5%
1 91
 
1.5%

Length

2024-05-26T17:47:53.456941image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-26T17:47:53.572135image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 5831
98.5%
1 91
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 5831
98.5%
1 91
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5831
98.5%
1 91
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5831
98.5%
1 91
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5831
98.5%
1 91
 
1.5%

English
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size92.5 KiB
1
5867 
0
 
55

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5922
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 5867
99.1%
0 55
 
0.9%

Length

2024-05-26T17:47:53.696148image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-26T17:47:53.813183image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1 5867
99.1%
0 55
 
0.9%

Most occurring characters

ValueCountFrequency (%)
1 5867
99.1%
0 55
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 5867
99.1%
0 55
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 5867
99.1%
0 55
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 5867
99.1%
0 55
 
0.9%

French
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size92.5 KiB
0
5066 
1
856 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5922
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5066
85.5%
1 856
 
14.5%

Length

2024-05-26T17:47:53.936183image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-26T17:47:54.053225image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 5066
85.5%
1 856
 
14.5%

Most occurring characters

ValueCountFrequency (%)
0 5066
85.5%
1 856
 
14.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5066
85.5%
1 856
 
14.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5066
85.5%
1 856
 
14.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5066
85.5%
1 856
 
14.5%

German
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size92.5 KiB
0
5152 
1
770 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5922
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5152
87.0%
1 770
 
13.0%

Length

2024-05-26T17:47:54.179872image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-26T17:47:54.295452image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 5152
87.0%
1 770
 
13.0%

Most occurring characters

ValueCountFrequency (%)
0 5152
87.0%
1 770
 
13.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5152
87.0%
1 770
 
13.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5152
87.0%
1 770
 
13.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5152
87.0%
1 770
 
13.0%

Hebrew
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size92.5 KiB
0
5855 
1
 
67

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5922
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5855
98.9%
1 67
 
1.1%

Length

2024-05-26T17:47:54.423086image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-26T17:47:54.539118image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 5855
98.9%
1 67
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 5855
98.9%
1 67
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5855
98.9%
1 67
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5855
98.9%
1 67
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5855
98.9%
1 67
 
1.1%

Hindi
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size92.5 KiB
0
5365 
1
557 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5922
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5365
90.6%
1 557
 
9.4%

Length

2024-05-26T17:47:54.662117image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-26T17:47:54.779117image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 5365
90.6%
1 557
 
9.4%

Most occurring characters

ValueCountFrequency (%)
0 5365
90.6%
1 557
 
9.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5365
90.6%
1 557
 
9.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5365
90.6%
1 557
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5365
90.6%
1 557
 
9.4%

Indonesian
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size92.5 KiB
0
5839 
1
 
83

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5922
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5839
98.6%
1 83
 
1.4%

Length

2024-05-26T17:47:54.908083image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-26T17:47:55.022120image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 5839
98.6%
1 83
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 5839
98.6%
1 83
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5839
98.6%
1 83
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5839
98.6%
1 83
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5839
98.6%
1 83
 
1.4%

Italian
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size92.5 KiB
0
5648 
1
 
274

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5922
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5648
95.4%
1 274
 
4.6%

Length

2024-05-26T17:47:55.147161image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-26T17:47:55.263128image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 5648
95.4%
1 274
 
4.6%

Most occurring characters

ValueCountFrequency (%)
0 5648
95.4%
1 274
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5648
95.4%
1 274
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5648
95.4%
1 274
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5648
95.4%
1 274
 
4.6%

Portuguese
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size92.5 KiB
0
5763 
1
 
159

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5922
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5763
97.3%
1 159
 
2.7%

Length

2024-05-26T17:47:55.389162image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-26T17:47:55.503128image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 5763
97.3%
1 159
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 5763
97.3%
1 159
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5763
97.3%
1 159
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5763
97.3%
1 159
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5763
97.3%
1 159
 
2.7%

Punjabi
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size92.5 KiB
0
5799 
1
 
123

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5922
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5799
97.9%
1 123
 
2.1%

Length

2024-05-26T17:47:55.626160image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-26T17:47:55.740161image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 5799
97.9%
1 123
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 5799
97.9%
1 123
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5799
97.9%
1 123
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5799
97.9%
1 123
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5799
97.9%
1 123
 
2.1%

Russian
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size92.5 KiB
0
5710 
1
 
212

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5922
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5710
96.4%
1 212
 
3.6%

Length

2024-05-26T17:47:55.864162image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-26T17:47:55.977163image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 5710
96.4%
1 212
 
3.6%

Most occurring characters

ValueCountFrequency (%)
0 5710
96.4%
1 212
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5710
96.4%
1 212
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5710
96.4%
1 212
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5710
96.4%
1 212
 
3.6%

Spanish
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size92.5 KiB
0
4744 
1
1178 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5922
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4744
80.1%
1 1178
 
19.9%

Length

2024-05-26T17:47:56.097758image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-26T17:47:56.213758image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 4744
80.1%
1 1178
 
19.9%

Most occurring characters

ValueCountFrequency (%)
0 4744
80.1%
1 1178
 
19.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4744
80.1%
1 1178
 
19.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4744
80.1%
1 1178
 
19.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4744
80.1%
1 1178
 
19.9%

Turkish
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size92.5 KiB
0
5848 
1
 
74

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5922
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5848
98.8%
1 74
 
1.2%

Length

2024-05-26T17:47:56.348742image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-26T17:47:56.493724image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 5848
98.8%
1 74
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 5848
98.8%
1 74
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5848
98.8%
1 74
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5848
98.8%
1 74
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5848
98.8%
1 74
 
1.2%

Ukrainian
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size92.5 KiB
0
5806 
1
 
116

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5922
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5806
98.0%
1 116
 
2.0%

Length

2024-05-26T17:47:56.618758image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-26T17:47:56.896841image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 5806
98.0%
1 116
 
2.0%

Most occurring characters

ValueCountFrequency (%)
0 5806
98.0%
1 116
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5806
98.0%
1 116
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5806
98.0%
1 116
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5806
98.0%
1 116
 
2.0%

Urdu
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size92.5 KiB
0
5100 
1
822 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5922
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5100
86.1%
1 822
 
13.9%

Length

2024-05-26T17:47:57.019627image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-26T17:47:57.138155image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 5100
86.1%
1 822
 
13.9%

Most occurring characters

ValueCountFrequency (%)
0 5100
86.1%
1 822
 
13.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5100
86.1%
1 822
 
13.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5100
86.1%
1 822
 
13.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5922
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5100
86.1%
1 822
 
13.9%

Interactions

2024-05-26T17:47:42.052301image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:15.471071image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:17.420393image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:19.282515image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:21.134825image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:22.949764image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:24.734336image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:26.414369image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:28.215528image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:30.217421image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:32.478857image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:34.308245image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:36.298871image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:38.342700image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:40.226350image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:42.176276image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:15.606072image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:17.553357image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:19.406547image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:21.256824image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:23.195574image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:24.850336image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:26.528496image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:28.339529image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:30.349549image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:32.610377image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:34.440236image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:36.429174image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:38.474669image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:40.348307image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:42.304289image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:15.737071image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:17.707474image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:19.536664image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:21.384862image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:23.322540image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:24.973335image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:26.654496image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:28.637619image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:30.495548image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:32.754377image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:34.580231image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:36.567171image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:38.594664image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:40.480074image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:42.437273image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:15.865104image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:17.843440image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:19.677656image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:21.516906image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:23.442540image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:25.094387image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:26.782540image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:28.783618image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:30.818093image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:32.892181image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:34.719232image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:36.702219image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:38.737707image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:40.609662image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:42.560268image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:15.989071image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:17.973446image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:19.807662image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:21.638914image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:23.560122image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:25.211381image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:26.908314image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:28.912617image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:30.943094image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:33.020168image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:34.862231image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:36.833210image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:38.875731image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:40.736725image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:42.670269image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:16.098151image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:18.089559image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:19.922657image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:21.752908image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:23.657124image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:25.311111image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:27.019325image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:29.020617image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:31.063095image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:33.127191image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:35.004266image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:36.947209image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:38.995707image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:40.847666image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:42.780334image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:16.208230image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:18.203547image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:20.037701image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:21.867904image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:23.760123image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:25.414111image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:27.127316image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:29.128771image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:31.230093image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:33.237187image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:35.123890image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:37.061251image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:39.111550image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:40.960667image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:42.889267image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:16.322194image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:18.317527image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:20.151699image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:21.978490image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:23.861122image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:25.515177image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:27.230316image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:29.237818image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:31.384248image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:33.344232image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:35.244860image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:37.186253image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:39.230550image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:41.071757image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:43.005299image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:16.438237image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:18.433314image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:20.272700image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:22.095531image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:23.966246image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:25.622197image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:27.410315image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:29.360862image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:31.535255image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:33.459238image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:35.370861image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:37.308252image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:39.350550image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:41.186788image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:43.117334image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:16.553563image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:18.555293image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:20.392699image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:22.216545image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:24.072297image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:25.728239image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:27.526314image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:29.486892image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:31.665791image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:33.574233image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:35.495862image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:37.427251image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:39.473550image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:41.305844image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:43.236365image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:16.670184image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:18.672281image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:20.511733image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:22.342531image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:24.178294image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:25.857272image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:27.642315image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:29.605975image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:31.804754image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:33.686232image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:35.620860image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:37.548959image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:39.594573image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:41.442824image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:43.362372image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:16.798186image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:18.807281image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:20.643699image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:22.472531image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:24.297294image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:25.976281image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:27.767498image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:29.737414image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:31.937752image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:33.815231image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:35.759862image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:37.683535image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:39.726171image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:41.572950image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:43.484413image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:17.031392image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:18.931280image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:20.771742image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:22.594531image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:24.410294image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:26.088341image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:27.883494image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:29.861907image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:32.064838image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:33.937232image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:35.908118image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:37.813535image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:39.853173image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:41.695951image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:43.607133image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:17.156359image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:19.052047image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:20.897762image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:22.719753image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:24.527335image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:26.203117image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:28.001497image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:29.986909image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:32.196840image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:34.074232image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:36.039124image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:37.945538image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:39.979219image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:41.826188image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:43.722135image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:17.288361image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:19.169044image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:21.016759image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:22.838770image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:24.632337image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:26.311368image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:28.110542image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:30.107422image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:32.346839image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:34.191232image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:36.181119image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:38.225548image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:40.110381image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-26T17:47:41.940089image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Missing values

2024-05-26T17:47:43.958168image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-26T17:47:44.717219image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-05-26T17:47:45.049292image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CategoryFieldSeller LevelSeller In Same LevelBasic PriceStandard PricePremium PriceBasic DeliveryStandard DeliveryPremium DeliveryBasic RevisionStandard RevisionPremium RevisionRatingRating CountCountryMember SinceAvg Response TimeLast DeliveryLanguageOrder in Queueis_single_planArabicBengaliChineseDutchEnglishFrenchGermanHebrewHindiIndonesianItalianPortuguesePunjabiRussianSpanishTurkishUkrainianUrdu
0Datadata-engineeringlevel 1293447.76626.86891.04714142225.03.0PakistanNaTNaN-1Urdu,English0False000010000000000001
1Datadata-engineeringlevel 1293250.00450.00950.003571255.05.0PakistanNaTNaN-1English,Urdu0False000010000000000001
2Datadata-engineeringlevel 129350.00100.00150.001350125.012.0PakistanNaTNaN-1Urdu,English0False000010000000000001
3Datadata-engineeringlevel 1293120.00200.00400.0023101225.013.0United StatesNaTNaN-1English0False000010000000000000
4Datadata-engineeringlevel 1293100.00300.00450.0071071124.68.0United KingdomNaTNaN-1English,Polish0False000010000000000000
5Datadata-engineeringlevel 129330.0050.0095.001231124.922.0PakistanNaTNaN-1Urdu,English,French0False000011000000000001
6Datadata-engineeringlevel 129380.00160.00300.0037143335.019.0BangladeshNaTNaN-1Bengali,English0False010010000000000000
7Datadata-engineeringlevel 1293100.002000.0010000.001232375.010.0PakistanNaTNaN-1English0False000010000000000000
8Datadata-engineeringlevel 1293200.00300.00400.00714211235.02.0United StatesNaTNaN-1English,Bulgarian0False000010000000000000
9Datadata-engineeringlevel 1293100.00100.00100.005551115.05.0PakistanNaTNaN-1Urdu,English0True000010000000000001
CategoryFieldSeller LevelSeller In Same LevelBasic PriceStandard PricePremium PriceBasic DeliveryStandard DeliveryPremium DeliveryBasic RevisionStandard RevisionPremium RevisionRatingRating CountCountryMember SinceAvg Response TimeLast DeliveryLanguageOrder in Queueis_single_planArabicBengaliChineseDutchEnglishFrenchGermanHebrewHindiIndonesianItalianPortuguesePunjabiRussianSpanishTurkishUkrainianUrdu
6245Businesssoftware-managementtop rated seller55150.01500.03000.0137-1-1-14.820.0Canada2020-12-011.03English,French0False000011000000000000
6246Businesssoftware-managementtop rated seller5550.0350.0650.0246-1-1-15.09.0Pakistan2018-10-011.00English,French,German,Spanish0False000011100000001000
6247Businesssoftware-managementtop rated seller5570.0130.0180.0101010-1-1-15.024.0South Africa2020-08-012.02English0False000010000000000000
6248Businesssoftware-managementtop rated seller55115.0155.0185.0101010-1-1-15.015.0South Africa2020-08-012.02English0False000010000000000000
6249Businesssoftware-managementtop rated seller55500.01250.03250.0101014-1-1-15.021.0Germany2020-07-013.04English0False000010000000000000
6254Businesssoftware-managementtop rated seller5515.060.090.0101010-1-1-15.04.0Mexico2015-04-018.00English,Spanish0False000010000000001000
6255Businesssoftware-managementtop rated seller55250.0250.0250.0101010-1-1-15.04.0Pakistan2018-09-011.06Urdu,English0True000010000000000001
6256Businesssoftware-managementtop rated seller55115.0155.0185.0101010-1-1-15.0128.0South Africa2020-08-012.02English0False000010000000000000
6260Businesssoftware-managementtop rated seller5550.050.050.0212121-1-1-15.036.0Bangladesh2015-03-011.07English0True000010000000000000
6261Businesssoftware-managementtop rated seller55150.01200.02250.051430-1-1-15.0175.0Canada2020-12-011.03English,French3False000011000000000000

Duplicate rows

Most frequently occurring

CategoryFieldSeller LevelSeller In Same LevelBasic PriceStandard PricePremium PriceBasic DeliveryStandard DeliveryPremium DeliveryBasic RevisionStandard RevisionPremium RevisionRatingRating CountCountryMember SinceAvg Response TimeLast DeliveryLanguageOrder in Queueis_single_planArabicBengaliChineseDutchEnglishFrenchGermanHebrewHindiIndonesianItalianPortuguesePunjabiRussianSpanishTurkishUkrainianUrdu# duplicates
0Businesssalestop rated seller3510.050.0180.0222-1-1-14.9219.0United States2017-01-012.00English,Japanese0False0000100000000000002
1Businesssalestop rated seller3515.050.0120.0124-1-1-14.9341.0Pakistan2020-02-011.00Urdu,English,Punjabi2False0000100000001000012
2Businesssalestop rated seller3520.035.045.0222-1-1-15.0331.0United States2017-01-012.00English,Japanese0False0000100000000000002
3Datadata-miningtop rated seller6530.030.030.02221001001005.071.0United States2020-12-017.00English,Spanish1True0000100000000010002
4Datadatabaseslevel 16085.020.060.01252334.82.0KenyaNaTNaN-1English0False0000100000000000002
5Datadatabasestop rated seller22300.0450.0600.0141414-1-1-1NaN-1.0CyprusNaTNaN-1English,Greek,Russian0False0000100000000100002
6Digital Marketingsocial-marketingtop rated seller45965.0115.0145.0445-1-1-15.0553.0Ireland2017-10-012.00English7False0000100000000000002
7Graphics & Designfashion-designtop rated seller1725.05.05.03332225.0417.0Indonesia2016-07-011.00English3True0000100000000000002
8Graphics & Designsocial-media-designtop rated seller25850.050.050.02225555.05291.0Romania2013-10-014.00English,Romanian4True0000100000000000002
9Graphics & Designsocial-media-designtop rated seller25850.065.090.03330004.93896.0United Kingdom2013-10-011.01English0False0000100000000000002